Does Fiscal Policy Matter Controlling for Money?Evidence from Panel Data Using War-Related Instruments

Bryan CaplanDepartment of Economics and

Center for the Study of Public ChoiceGeorge Mason University*

JEL Classifications: E63, E52, N10Keywords: monetary vs. fiscal policy, structural VARs, war

Abstract:

Recent empirical studies of the effects of fiscal policy generally fail to control for money. This is especially problematic if one uses war-related variables to identify exogenous shocks (Ramey and Shapiro [1997]; Edelberg, Eichenbaum, and Fisher [1998]), because both fiscal and monetary policy are usually more expansionary during wars. The current paper treats wars as macroeconomic "natural experiments" to see if fiscal policy matters holding monetary policy constant. Estimation on two distinct pooled time series (one with 15 industrialized countries from 1881-1988, the other with 69 diverse countries from 1950-1992) yields similar results: money always has positive and significant impacts on both nominal and real output, but fiscal shocks never have a positive, significant impact on either nominal or real output.

* Bryan Caplan, Department of Economics, George Mason University, Fairfax, VA 22030; email: bcaplan@gmu.edu; phone: 703-993-2324; fax: 703-993-2323. I would like to thank Michael Bordo for discussion and generous provision of data, as well as Anne Case, Harvey Rosen, Ben Bernanke, Alan Blinder, Tyler Cowen, Bill Dickens, Alex Tabarrok, and seminar participants at George Mason for helpful comments and suggestions. Gisele Silva provided excellent research assistance. The standard

disclaimer applies.

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1. Introduction

U.S. defense spending as a fraction of GDP fell to a post-war low of 4.3% in the last

quarter of 1997. In that same quarter, the U.S. unemployment rate stood at 4.7%, its

lowest level in over twenty five years. Ten years ago, defense as a fraction of output

was nearly twice at large as it is today. Not only is the impact of the cuts on real

output or employment difficult to discern; even the growth of nominal output seems

unaffected. While rational expectations about this ten-year downward path for

defense spending could explain the absence of a real impact on output or

employment, defense cuts do not seem to have depressed even nominal output

growth.

Most economists in the post-war period have accepted the expansionary impact of

fiscal policy on both real and nominal output: in a recent survey, 59.3% "generally

agreed," and 30.6% more "agreed with provisos" that "fiscal policy has a significant

stimulative impact on a less than fully-employed economy." (Alston, Kearl, and

Vaughan [1992], p.204) The experience of the United States during World War II in

particular has often been adduced as decisive evidence that strongly expansionary

fiscal policy can generate large increases in real output and employment.1 Yet since

the 1960's discretionary fiscal policy has fallen out of favor with economists across

the political spectrum, a development analyzed by Eichenbaum (1997):

The inability to find a satisfactory way of formulating discretionary fiscal policy as an implementable rule and a set of practical institutions to support that rule has led even most Keynesians to be skeptical of attempts to use discretionary fiscal policy to stabilize business cycles. It is an interesting curiosity that Keynesians and real-business-cycle (RBC) analysts agree that, in principle, increases in government purchases and decreases in distortionary taxes increase aggregate employment and output, at least in the short run... The problem is that

1 For an overview of academic perceptions of World War II, see Higgs (1992).

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countercyclical fiscal policy has to be implemented in the context of a particular institutional environment. Even if policymakers had the hubris to think that they knew just when and how much expansionary fiscal policy to apply, the lags inherent in the institutions for setting fiscal policy are such that it never happens in either the desired quantity or the desired time frame. (p.237)

In sum, discretionary fiscal policy has been largely abandoned on the pragmatic

ground that it is hard to use in democracies rather than the principled ground that it

does not work.2 The primary purpose of this paper is to investigate the stronger

claim that fiscal policy however skillfully used does not expand real or nominal output

holding monetary policy constant. This point is critical because recent studies of

fiscal policy do not generally control for money. (Edelberg, Eichenbaum, and Fisher

[1998], Blanchard and Perotti [1998], Ramey and Shapiro [1997], Braun and

McGrattan [1993], Rotemberg and Woodford [1992])

Looking at the effect of fiscal policy in isolation seems particularly inappropriate when

one uses war-related variables to achieve identification because both fiscal and

monetary policy are ordinarily expansionary during wars and military buildups.

(Caplan [1999]) Like Edelberg, Eichenbaum, and Fisher (1998), Ramey and Shapiro

(1997), and a number of other papers, the current paper uses wars and other war-

related variables as instrumental variables. But the current paper treats these

instruments as exogenous shifters of both fiscal and monetary policy. It then uses

the estimated impact of exogenous policy to separately calculate impulse-response

functions for monetary and fiscal policy shocks.

One possible concern about using war-related variables as instruments is that most

of the empirical work along these lines focuses on a small set of countries and/or

2 Blinder (1997) provides a more detailed argument for the advantages of Fed-like policy-making agencies of independent experts over politicized bodies like Congress.

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episodes. This leaves open the possibility that confirmations get excess attention

while counter-examples are ignored. To address this concern the current paper uses

panel data for a large number of economies over long timespans, similar to the

approach in Bordo and Jonung (1996), Bordo (1993), and Backus and Kehoe (1992).

To further check the results' robustness, whenever possible all tests are performed

on both a "narrow" data set of 15 countries from 1881-1988, and on a "broad" data

set of 69 countries from 1950-1992.

The paper is organized as follows. The second section discusses and critiques

recent literature on fiscal policy. The third section explains how the two distinct data

sets used throughout the paper were assembled. The fourth section briefly

examines the stylized facts about economic performance during wartime to double-

check the appropriateness of using war-related factors as instrumental variables.

The fifth section sets up the baseline specification, estimates the impact of monetary

and fiscal policy shocks on nominal and real output, and reports the results for both

data sets. The sixth section checks the sensitivity of the results to specification

changes. The seventh section concludes the paper and discusses avenues for

future research.

2. Related Literature: Review and Critique

While empirical study of the nominal and real effects of macro policy has advanced

with renewed vigor in recent years, monetary policy has received the lion's share of

the attention, especially in studies using structural VAR (SVAR) methodology.

(Bernanke and Mihov [1998b], Leeper, Sims, and Zha [1996] Christiano,

Eichengreen, and Evans [1996], Bernanke and Blinder [1992]) The SVAR

literature's findings are mostly consistent with simple New Keynesian nominal rigidity

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models: usually some measure of monetary policy has the expected positive impact

on output and employment, and other real variables.3 Probably the most prominent

alternative econometric strategy has been the narrative approach. (Romer and

Romer [1989], [1994a], [1994b], Boschen and Mills [1995]) The idea is to use the

historical record to distinguish exogenous from endogenous policy; for example,

Romer and Romer (1989) develop a dummy variable to index conscious shifts in

Federal Reserve policy. Overall, the findings of narrative studies also tend to

support the validity of simple New Keynesian nominal rigidity models.

Parallel literatures on fiscal policy has been slower to develop. Bernanke (1986)

includes military spending as a variable, and Romer and Romer (1994a) uses the

Romer index and the related Boschen-Mills index4 as instrumental variables for fiscal

as well as monetary policy. But a working paper by Blanchard and Perotti (1998)

appears to be the first focused application of SVAR methodology to taxation and

spending. Blanchard and Perotti's reported findings for the U.S, U.K., and Canada

generally suggest a dynamic spending multiplier slightly in excess of 1, and a

dynamic multiplier for net transfers slightly below 1.5 Similarly, Ramey and Shapiro

(1997) appears to be the first to apply the Romers' narrative technique to design

instruments to capture the impact of fiscal policy. They argue that their set of

"military buildup dummies" for the U.S. economy makes it possible for them to

3 At the same time, several anomalies have appeared repeatedly in the SVAR literature, especially the "price puzzle" and the "liquidity puzzle"; Leeper, Sims, and Zha (1996) provides a detailed discussion. There have been several attempts to resolve the puzzles by adding more variables to the estimated system, or using a different measure of monetary policy: see e.g. Christiano, Eichenbaum, and Evans (1996), Strongin (1995), Leeper and Gordon (1992), and most recently Bernanke and Mihov (1998b).

4 The Boschen-Mills index attempts to measure the absolute stance of monetary policy, using a discrete scale that ranges from +2 (most expansionary) to -2 (least expansionary). (Boschen and Mills [1995])

5 These results differ from their initial findings, reported in Blanchard (1997), which found the usual impact of taxation but no discernable impact of spending.

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identify a significant effect of exogenous fiscal policy on real output.

It has long been understood that if monetary authorities accomodate changes in

fiscal policy, fiscal policy will appear to have real effects even if it is monetary policy

that really does the work. (Friedman and Heller 1969) My central reservation about

these recent findings on fiscal policy is that the econometric specifications almost

completely ignore monetary policy, usually because the underlying RBC theoretical

framework rules out real effects of money a priori.6 (Edelberg, Eichenbaum, and

Fisher [1998], Ramey and Shapiro [1997], Braun and McGrattan [1993], Rotemberg

and Woodford [1992]) The New Keynesian findings of the VAR and narrative studies

of monetary policy make this omission all the more puzzling. If empirical studies of

money confirm its real effects, why should empirical studies of fiscal policy assume

the opposite? This is even more questionable when using war-related variables as

instruments: Ordinarily, wars make monetary and fiscal policy more expansionary, so

ignoring monetary policy is particularly prone to upwardly bias estimates of fiscal

policy's effect. Like Ramey and Shapiro, the current paper uses war-related

variables as instruments, but it applies them to both monetary and fiscal policy.

A deeper problem with recent fiscal policy literature is the theoretical framework. If

monetary policy has real effects due to New Keynesian nominal rigidities, why is it

necessary to appeal to implausible RBC mechanisms for fiscal policy? (Mankiw

1989) Perhaps fiscal policy - like monetary policy - has real effects simply due to

New Keynesian nominal rigidities, as it would in the textbook ISLM model. (Romer

1993; Mankiw 1990) Accordingly, the current paper focuses on nominal rigidity

6 A notable exception is Blanchard and Perotti (1998), which does not control for monetary policy even though it has no RBC presuppositions.

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channels for both fiscal and monetary policy. It considers RBC channels only later

when checking the results' sensitivity.

3. The Data

As a check on the robustness of the results, the current paper performs all tests on

two distinct data sets: the "broad" data set of 697 countries over the period from

1950-1992, and the "narrow" data set of 15 countries8 over the period from 1881-

1994. The 15 countries in the "narrow" data set are all relatively advanced

industrialized nations, while the 69 countries include advanced industrialized nations,

LDCs, and couple of Communist and former Communist countries.

Most of the "broad" data set comes from combining the Annual Data on Nine

Economic and Military Characteristics of 78 Nations, 1948-1983 (ICPSR 9273) with

World Military Expenditures and Arms Transfers, 1983-1993 (ICPSR 6516).9 Both

series measure output in current dollars. To calculate real output, series were

converted to constant dollars; to calculate nominal GDP, the current dollar figures

were multiplied by current exchange rates into domestic currency. Matching data for 7 The "broad" data set is comprised of the following countries: USA, UK, Austria, Belgium, Denmark, France, West Germany, Italy, Netherlands, Norway, Sweden, Switzerland, Canada, Japan, Finland, Greece, Iceland, Ireland, Malta, Portugal, Spain, Turkey, Yugoslavia, Australia, New Zealand, South Africa, Argentina, Bolivia, Brazil, Chile, Columbia, Costa Rica, Dominican Republic, Ecuador, El Salvador, Guatemala, Haiti, Honduras, Mexico, Nicaragua, Panama, Paraguay, Peru, Uruguay, Venezuela, Iran, Iraq, Israel, Jordan, Lebanon, Saudi Arabia, Syria, Egypt, Afghanistan, Burma, Sri Lanka, India, Indonesia, South Korea, Nepal, Pakistan, Philippines, Thailand, Ethiopia, Liberia, China, Hungary, Poland, and Romania. The following nine countries from the same data sets (Annual Data on Nine Economic and Military Characteristics of 78 Nations, 1948-1983 [ICPSR 9273] and World Military Expenditures and Arms Transfers, 1983-1993 [ICPSR 6516]) were omitted due to missing data: Yemen, Albania, Bulgaria, Cuba, Czechoslovakia, East Germany, Mongolia, North Korea, and the USSR. Omitting the few remaining Communist and former Communist countries too does not significantly change the subsequent results.

8 The "narrow" data set is comprised of the following countries: USA, UK, Germany, France, Japan, Canada, Italy, Belgium, Netherlands, Switzerland, Denmark, Finland, Norway, Sweden, and Portugal.

9 When the measurements for the overlapping year (1983) differed, the latter series was multiplied by a constant to make the two equal at the spline point.

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M2 comes from the appropriate volume of International Historical Statistics,

supplemented by the International Financial Statistics Yearbook.10 Missing

information on exchange rates was supplied by the Pennworld data set.11

The "narrow" data set was provided courtesy of Michael Bordo, as compiled in

several of his earlier studies. (Bordo and Jonung [1996], Bordo[1993]) Bordo's

money supply data uses M2 if it available over a sufficiently long period, and M1

otherwise. Data on fiscal variables matching Bordo's data set was found in various

volumes of International Historical Statistics.

Most recent studies of money find that interest rates are a better measure of the

stance of policy than monetary aggregates, (Bernanke and Blinder [1992]) but they

have also found the superior metric to be institution-specific. (Bernanke and Mihov

[1998a]) If non-monetary forces impinge on short-term interest rates - as they would

in environments with large fiscal shocks - then monetary aggregate measures are

probably the better measure. Keeping interest rates constant in the face of loose

fiscal policy is expansionary - not neutral - monetary policy. Moreover, interest-rate

measures are more credible in relatively stable monetary regimes; they are much

less meaningful in the inflationary and deflationary environments which appear fairly

frequently in my data sets. For example, nominal interest rates for the U.S. were

comparably low during the Great Depression and World War II, even though

monetary policy was extremely tight in the first case, extremely loose in the second. 10 When there was a change in the definition of a variable, or when it was necessary to supplement data from International Historical Statistics with data from International Financial Statistics Yearbook, the later measurements of the series were multiplied by a constant to make the divergent series equal at the spline point.

11 When there was a conflict between the two exchange rate measurements - almost always in fixed exchange rate regimes - the Pennworld data showing continuous "unofficial" changes in the exchange rate was used.

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For both of these reasons, the current study measures the direction of policy using

monetary aggregates instead of interest rates.

The data on participation, dates, and battle deaths in wars all come from the

Correlates of War Project: International and Civil War Data, 1816-1992. Since the

Correlates of War records even extremely minor military incidents, my dummy

variable War only "turns on" if both (battle deaths/population) and (battle

deaths/population/year) exceeded 1 in 100,000. This excludes both extremely long-

term, low-intensity conflicts as well as extremely short high-intensity ones. Foreign

(a variable equal to 1 if a war was fought exclusively on foreign soil and 0 otherwise)

is derived from the information provided from the Correlates of War, with ambiguous

cases resolved by examining historical atlases.

As many country-years of data as possible were included, with one exception:

country-years of hyperinflation (defined as country-years with nominal output growth

in excess of 100%) were excluded from most estimation. Hyperinflation very rarely

occurred in the narrow data set12, but was fairly common in the broad data set. A

wide body of theory and empirical research suggests that economies' response to

high inflation is quite different from their response to more moderate doses; see e.g.

Engsted (1994), Christiano (1987), Sargent (1982), Sargent and Wallace (1973), and

Cagan (1956).

For current forecasting and short-run policy-making purposes, there is a strong

argument that it is better to rely on relatively recent data from the single country one

12 The data for the post World War I European hyper-inflation in Germany was missing in the 15-country pool, whereas there was quite complete data on several hyper-inflationary regimes among the LDC's.

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is interested in, rather than heterogeneous pooled time series of the kind used here.

The econometric approach of this paper has a comparative advantage along another

margin. If researchers tend to study data sets that are likely to confirm their priors,

results may suffer from "selection bias." Double-checking their results against

diverse panel data is a wise precaution. General macroeconomic theories about e.g.

the effects of fiscal policy ought to hold in a wide variety of countries and time

periods if they are true at all.

4. How Does War Shock the Macroeconomy?

The core of this paper appeals to the stylized facts about wartime to achieve

identification. Most central are the facts that fiscal and monetary policy are

expansionary during wartime, and wartime expansionary policy leads to above-

average rates of growth of nominal and real output.13 This section double-checks the

putative stylized facts. It also experiments with different measures of wartime

conditions to check the results' sensitivity and discover the most revealing

instruments.

The investigations begin by separately estimating the equations:

(1)

(2)

(3)

(4)

13 Higgs (1992) argues that contrary to the textbook Keynesian account, U.S. living standards declined during World War II: due to widespread rationing and other price index and real output measurement problems, real consumption was starkly lower than usually estimated. While Higgs' argument shows that real output statistics are misleading indices of consumers' standard of living during wartime, in my view standard statistics remain reasonable measures of the level of production. The problem is essentially that during wartime the link between measured production and actual consumption becomes much weaker than during peacetime. With this caveat in mind, the current paper still uses standard real output statistics for wartime periods.

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where N is the growth rate of nominal output, R is the percentage change in real

output, M is the percent change in the money supply, and Gfrac is total government

spending as a fraction of GDP. X is a vector of country and year dummies, War is a

dummy variable equal to 1 if a country was at war in a given year and 0 otherwise,

is the error term, and the remaining variables are parameters.

The regressions were performed on both data sets. The data is sampled to preserve

comparability with the baseline results in the next section, so the first three country-

years for each country, country-years with N>100%, and country-years with missing

observations for N, R, M, or Gfrac are excluded. The initial results - shown in the

first blocks of Tables 1a and 1b - seem disappointing: the only variable that

consistently rises during wartime periods appears to be government spending as a

fraction of output. Money supply growth does not significantly increase, and neither

do real or nominal output.

To check the sensitivity of this result, the wars were broken into two distinct classes.

Foreign was defined as =1 if all of the wars a country was engaged in during a given

year were exclusively fought on foreign soil, and 0 otherwise. Foreign and (1-

Foreign) were then interacted with War to yield DomwarWar*(1-Foreign) and

ForwarWar*Foreign. Domwar=1 if a country fought a war on its home soil during a

given year and 0 otherwise; Forwar=1 if a country fought wars during a given year,

but these were exclusively on foreign soil. During years of peace, of course,

Domwar=Forwar=0. (1) through (4) were then re-estimated, allowing for different impacts of the

two kinds of wars:

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(1')

(2')

(3')

(4')

This slight change in specification drastically alters the results, revealing several

consistent patterns over both data sets. The results appear in the second blocks of

Tables 1a and 1b. Both data sets show large declines in real output growth during

domestic wars, and smaller but still statistically significant increases in real output

growth during foreign wars. The magnitudes of the effects on real output still differ

somewhat between the two data sets: the impact of foreign wars on real growth is

more positive, and the impact of domestic wars is less negative, for the broad data

set than for the narrow. But the results are qualitatively similar.

Separately estimating the impact of foreign and domestic wars also changes the

results for nominal output and monetary and fiscal policy. While the patterns in the

two data sets differ, in each there is a subset of wars in which both monetary and

fiscal policy are strongly expansionary, and nominal output rises. In the narrow set's

domestic wars, money growth is typically 6.6%, government spending as a fraction of

GDP is 6.7 percentage-points, and nominal output growth 4.4 percentage-points

higher than normal. In the broad set's foreign wars, money growth is 4%,

government spending as a fraction of GDP is 1.9 percentage-points, and nominal

output growth 5.1 percentage-points above the norm.

The puzzling failure of government spending to increase in the broad data set can be

resolved, as Table 1c shows. The broad data set (unlike the narrow) breaks down

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government spending into military and nonmilitary components. Separately

estimating the response of these two types of spending to war-related variables

shows that military spending always increases significantly during wartime. During

domestic wars this is usually accompanied by cuts in non-military spending: military

spending as a fraction of output rises by 1.1 and non-military spending falls by 1.5

percentage-points. During foreign wars military spending rises by an estimated 3.6

percentage-points, with a statistically insignificant .5 fall in non-military spending.

One important possibility is that interacting Foreign with War may merely be

capturing the effect of the destructiveness of a given war, which could be better

measured by a different, continuous variable. To allay this concern, I constructed a

casualty-rate variable, similar to that found in Barro (1981).14 Casualty was then

interacted with Foreign to yield Domcas and Forcas. Re-running the previous

regressions with Domcas and Forcas does not eliminate the explanatory power of

the discrete variables Domwar and Forwar. The import of Casualty is sensitive to the

choice of data set, although in both data sets it makes some degree of difference.

This suggests the desirability of using both variables as instruments - a suggestion

that will be followed in the next section.

5. The Impact of Policy on Nominal and Real Output

a. The Baseline Specification

The central goal of this paper is to use the two data sets in tandem to determine if fiscal policy matters

holding monetary policy constant. These questions can be answered by estimating an identified

14 Note that whereas Barro (1981) had annual estimates of casualties, the Correlates of War series provides only countries' war participation duration, population (pre- and post-war), and total battle deaths. Casualty in a year t is therefore defined as the number of battle deaths in a given war divided by the pre-war population (expressed in 1000's), times the number of months of the war during year t, divided by the total number of months the country was involved in that war.

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version of the general structural model like that of Bernanke and Blinder (1992):

(5)

(6)

where Y is a vector of non-policy variables, and P is a vector of policy variables.

The initial specification uses four important restrictions to identify the model. The

first three restrictions make assumptions about the effects of war-related variables

that the the previous section's results suggest are sensible:

Restriction #1: The occurrence of wars themselves is exogenous. While it may be interesting

in later research to test models in which war occurs endogenously (Hess and Orphanides

[1995]), the assumption of exogeneity is maintained throughout the paper.

Restriction #2: To account for the possible impact of destructive wars on aggregate supply,

casualty rates (Forcas and Domcas) are allowed to directly affect real - but not nominal -

output.

Restriction #3: Neither foreign nor domestic wars (Forwar or Domwar) have a direct impact

on either non-policy variable. If war affects nominal or real output, it does so by changing

policy variables, which in turn alter the non-policy variables.

The fourth restriction specifies that New Keynesian nominal rigidities are the relevant

mechanism by which policy affects real output.15 Intuitively, suppose that a shock to

15 See e.g. Romer (1993), Gordon (1990), and Mankiw (1990). As Gordon puts it, "The task of new-Keynesian economics is to explain why changes in the aggregate price level do not mimic changes in nominal GNP. Sticky prices imply that real GNP is not an object of choice by individual workers and firms but rather is cast adrift as a residual." (p.1128) Subsequent sensitivity tests replace this New Keynesian assumption with an RBC substitute.

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M or F fails to affect nominal output. Then nominal rigidity models give no reason to

think that real output will rise either: if nominal spending stays the same, then a real

economy with nominal rigidity has no tendency to expand:

Restriction #4: If policy affects real output, it does so by changing nominal output.

The general model thus consists of the following system of equations (7)-(10):

where X remains a vector of country and year dummies, N, R, and M are the

percentage-changes in nominal output, real output, and the money supply, and

Domwar, Forwar, Domcas, and Forcas are defined as above. F, the measure of

fiscal policy, is the change in Gfrac, government spending as a fraction of output.16

The third and fourth equations for the monetary and fiscal reaction functions remain

unidentified. However, it is possible to estimate (7) and (8), the two identified

equations of this partially identified system. These two identified equations for

nominal and real output are the ones of central interest since they show the impact of

exogenous policy shocks on the non-policy variables.17 In other words, this paper

uses war-related instruments to estimate how any exogenous policy shock affects

the economy; it is not focused on the economic effects of war. Once the

instrumental variables identify the crucial structural variables, it is possible to answer

this paper's central question by examining how the system responds to exogenous 16 Since the war-related variables positively shift Gfrac in levels, F will be positively shifted only by changes in war-related variables. This does not require a specification change because lags of the war-related variables are included.

17 Given the long duration and heterogeneity of the countries in the two data sets, a stable response of non-policy variables to policy shocks is more plausible than stable policy-makers' reaction functions.

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fiscal and monetary impulses.

To sum up the key features of the "baseline specification": The equations for nominal

and real output are estimated using 3SLS subject to restrictions 1-4. The baseline

specification uses 3 lags. Current and lagged Domwar, Forwar, Domcas, and

Forcas serve as instruments, along with lags of all four endogenous variables and

the full set of country and year dummies. Hyperinflation country-years, defined as

country-years in which N>100%, are excluded. From the results for the baseline

specification, it is possible to derive the implied impulse-response functions for

various exogenous policy shocks, along with the associated standard error bands.

b. The Baseline Results

Tables 2a and 2b show the estimated coefficients for the baseline specification,

omitting the country and year dummies. The results for the narrow and the broad

data set are markedly similar, although those of the narrow data set are more

precise. In both cases, there is a positive contemporaneous correlation between

nominal and real output, and a negative correlation between real output and lagged

nominal output, suggesting the presence of an expectations-augmented Phillips

curve. In both cases, there is a positive impact of contemporaneous money on

nominal output. Finally, in both cases the nominal impact of innovations to

government spending as a fraction of output appears if anything to be negative.

Study of some associated impulse-response functions permits a more rigorous

analysis of the implied dynamics of the baseline results. Figure 1a shows the narrow

data set's implied response of nominal output to a permanent 1% increase in the rate

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of money supply growth.18 Figure 1b repeats the same policy experiment for the

broad data set. The +2 and -2 SE bands are calculated using numerical derivatives

and the approximation f'(b)'V f'(b).

The impact of greater money growth on nominal output is plain in both data sets.

The theoretically predicted 1% increase in nominal output growth lies comfortably

within the shown SE bands. The main difference between the data sets is that the

nominal impact of money is larger and less precisely estimated for the narrow; for the

first four years of the policy experiment, zero effect lies within the SE bands even

though the point estimate of the change in nominal output exceeds 1%. In contrast,

the results for the broad data set have smaller SE bands, such that the -2 SE

boundary always lies above the x-axis. After about ten years, the estimated impact

of money stabilizes at 1.65%±.82% for the narrow data set, and .92%±.37% for the

broad data set.

Figures 2a and 2b show the corresponding impulse-responses of the permanent 1%

increase in money supply growth to real output. Once again, both data sets yield

similar qualitative conclusions: money increases real output. The SE bands are

initially too large to reject the null of zero impact, but the estimates soon become

more precise. The short-run real effect of money is larger than the long-run. The

main puzzle is that the real impact of money does not seem to dampen down to zero

even over long time horizons: the "steady-state" real effect of an extra 1% money

growth is .75%±.61% and .33%±.21% for the narrow and broad sets respectively.

The same inconsistency with the long-run neutrality of money appears repeatedly in

the structural VAR literature. Subsequent sensitivity tests in this paper explore the 18 Focusing on this permanent shock makes the results more transparent than they are for the corresponding temporary shock.

17

robustness of this anomaly; see Bernanke and Mihov (1998b) for a discussion of

long-run neutrality in the recent empirical monetary literature along with an attempt to

resolve the puzzle.

The first policy experiment checked the impact of a permanently higher money

growth rate on nominal and real output, holding fiscal policy constant. What would

the impact of expansionary fiscal policy be, holding monetary policy constant?

Impulse-response functions were computed for the economy's response to a

permanent 1% increase in F, i.e., the impact of repeatedly raising government

spending as a fraction of output by 1%. This fiscal experiment is extremely

expansionary: over a 30-year period, it would require e.g. government spending

rising from 40% of output to 70%.19

Figures 3a and 3b show how this experiment would change nominal output. The

results for both data sets are similar: the point estimate for the impact of government

spending is always negative. The +2 SE bands leave open the possibility of some

positive impact, but the -2 SE bands suggest that government spending might even

have a large negative effect on nominal GDP. In both data sets, the null of zero

nominal impact of government cannot be rejected. The "steady-state" consequence

of the fiscal policy experiment is -.27%±1.00% for the narrow data set and -1.34%

±2.15% for the broad. In short, a drastic fiscal policy experiment's expansionary

impact even on nominal output is difficult to detect in either of the data sets.

The inability of government spending to increase real output should be anticipated

given the restrictions and the weak evidence for the nominal consequences of 19 Again, the analysis focuses on this admittedly unrealistic permanent shock rather than a temporary shock in order to make the results clear.

18

expansionary fiscal policy. Figures 4a and 4b show the response of real output to

the fiscal policy experiment. Once again, it is not possible to reject the null of zero

effect. The point estimates of the real impact of fiscal policy are almost always

negative, and the "steady-state" impact is estimated at -.12%±.46% for the narrow

set, and -.47%±.75% for the broad.

The baseline results suggest there is nothing unusual about the recent economic

performance of the U.S. economy. Holding money growth constant, strong growth of

real and nominal output are at least as likely to be found in an era of steady defense

cuts as in the middle of an arms race. The baseline results also indicate that the

SVAR and narrative studies' estimates of the nominal and real impact of monetary

policy generalize to a wide variety of countries and time periods. If there is bias

towards over-studying countries in which money seems to matter, this bias does not

seem to have biased the final estimates. Moreover, even though the monetary

transmission mechanism (Mishkin [1995]) would have to differ between countries

and over time, money seems to matter in a wide variety of countries and historical

eras.

c. Comparison with Other Recent Results

The current paper's baseline results for monetary policy conform to the SVAR

consensus. However, my results for fiscal policy differ somewhat from the recent

literature's. Blanchard and Perotti (1998) find a significant impact of fiscal policy on

real output, with an implied spending multiplier slightly above 1, and an implied net

transfers multiplier slightly below 1.20 Ramey and Shapiro's (1997) implied spending

20 Since Blanchard and Perotti did not include a price or nominal income variable, it is not possible to compare my results for nominal output to theirs.

19

multiplier seems to exceed 1 for about two years, falling to 0 after about 3 years.

Edelberg, Eichenbaum, and Fisher's (1998) results are fairly similar, except that the

spending multiplier remains in excess of 0 (but less than 1) even after four years.21

As mentioned earlier, Romer and Romer (1994a) concentrates on monetary policy

but produces results for fiscal policy as well. Their simple OLS results show a net

transfers multiplier around 1. But interestingly, Romer and Romer's IV estimation,

using the Romer index and the Boschen-Mills index as instruments, shows no impact

of fiscal policy. The point estimate in fact tends to be slightly negative. Romer and

Romer downplay this result by pointing to the large standard error bands, but it is

noteworthy that I reach similar results using a different econometric strategy.

If fiscal policy does not matter holding monetary constant, why do recent studies

continue to find an effect? One possibility - frequently raised during the vintage

Keynesian-monetarist debates22 - is that monetary policy is frequently not held

constant when fiscal policy changes. In particular, in nominal interest rate targeting

regimes, more expansionary fiscal policy will normally provoke an accommodating

response from monetary policy. To check this possibility, my baseline results were

re-run with the monetary variables omitted. The measured impact of government

spending generally became visibly more positive in this specification, although the

SE bands remained too large to reject a null of zero effect. Perhaps other studies

are merely picking up the accomodation of monetary policy. It is also worth noting

21 Braun and McGrattan (1993) and Rotemberg and Woodford (1992), using calibration methods rather than SVARs, reach similar conclusions. Ramey and Shapiro and Edelberg, Eichenbaum, and Fisher use VARs for empirical estimation, then compare their results to calibrated simulations.

22 See e.g. Friedman and Heller's (1969) classic exchange.

20

that when Edelberg, Eichenbaum, and Fisher modify their specification by adding M1

to the variables in their VAR, they find that M1 permanently increases by about two

percentage-points during military buildups, and nominal interest rates initially decline.

This is definitely consistent with a model where only money matters for nominal and

real output, but monetary policy typically accomodates fiscal expansions.23

While the estimated impact of fiscal policy does vary between the current paper and

other recent research, all stand in sharp contrast to estimates formed prior to the

breakdown of the Keynesian consensus. These tended to be much higher. Blinder

and Solow (1974), surveying a wide range of contemporary models, found that

except for the monetarist St. Louis model, the cumulative multiplier for government

spending's effect on nominal output varied from 1.8 to 3.0.24 A critical review of

Blinder and Solow's work found that macro models' predicted impact of government

spending on nominal output implied a multiplier in the range of 1.8-4.3, but with more

ambiguous effects on real output. (Infante and Stein [1976]) It is noteworthy that

recent research converges at estimates well below the lower bound of the non-St.

Louis models surveyed by Blinder and Solow.

6. Sensitivity Tests

Four sorts of sensitivity tests were conducted. The first group examined the

consequences of using different measures of fiscal policy. The second checked the

sensitivity of the final results to the number of lags. The third experimented with the

choice of instrumental variables and the method of system estimation. The fourth

looked at the impact of replacing the New Keynesian Restriction #4 with an RBC 23 One way to test this interpretation of their results would be to replace real defense purchases with M1, then see whether real output still rises during military buildups.24 More recently, Eisner (1989) has found comparably large effects of fiscal policy.

21

variant.

a. Alternative Measures of Fiscal Policy

The baseline specification only uses the change in government spending as a

fraction of output to measure the stance of fiscal policy. In RBC-type models (e.g.

Barro [1987]) this is the correct measure to use unless taxes are distortionary. But

most New Keynesian theories would imply that tax increases counteract the

expansionary impact of government spending. Tax collections as a fraction of output

generally do increase during war, but government spending's increase normally far

outstrips the increase in taxation. Even so, omitting taxation from the baseline

specification leaves open the possibility that the expansionary impact of deficit-

financed government spending is underestimated.

A fifth endogenous variable T - the change in taxation as a fraction of output - and a

fifth equation for T, is added to the system of equations (7)-(10). Once again, the

system is partially identified, so it is only possible to estimate the two equations for

the nonpolicy variables. After estimating this system using 3SLS, impulse response

functions for three policy experiments were conducted. The first policy experiment

permanently raises the rate of money supply growth by 1%; the second policy

experiment permanently raises the change in government spending as a fraction of

output by 1%; the last experiment permanently raises the change in taxation as a

fraction of output by 1%.

The results are qualitatively the same for both data sets; the narrow data set's results

appear in Figures 5a-f, while those for the broad data set appear in Figures 6a-f.

Controlling for taxation mainly makes the contrast between monetary and fiscal

policy starker. The SE bands for money shrink so that the lower SE bands always lie

22

above the x-axis, and the point estimates for the impact of money fall to more

plausible levels. Yet neither measure of fiscal policy matters much. The estimated

impact of government spending on nominal and real output remains negative, and it

is never possible to reject the null of zero. The point estimates for the effect of

taxation at least have the expected negative sign, but it is also not possible to reject

the null that taxation has no real or nominal consequences.

A second possible problem with F as a measure of fiscal policy is that it treats

military and nonmilitary government spending symmetrically. Barro (1987; 1986;

1981) in particular uses wartime military expenditures to get at the differential impact

of temporary and permanent shifts in fiscal policy. The narrow data set does not

contain separate measurements for military and nonmilitary spending, but the broad

data set does. Redoing the baseline results on the broad data set while allowing

these two kinds of spending to have different effects slightly reduces the estimated

impact of monetary policy and reduces the size of the SE bands (Figures 7a and 7b).

More significantly, respecification unveils differential impacts of military and

nonmilitary spending (Figures 7c-7f). A buildup in nonmilitary spending has

approximately zero impact on nominal and real output in the economic as well as

statistical sense. A military buildup, in contrast, seems to have the statistically

significant negative effect on both nominal and real output shown in Figures 7a and

7b. The analysis of Barro and others on temporary vs. permanent fiscal shocks

makes these results puzzling; one would expect that military spending with its larger

temporary component would at least have a more positive effect.

b. The Number of Lags

The baseline results were re-run with six lags instead of three. In both cases, the

23

point estimates from the impulse-response functions declined in absolute value, and

the SE bands became markedly smaller. Qualitatively, the 6-lag results differ little

from the 3-lag results; quantitatively, the results derived from the narrow and the

broad data set become more similar. The main difference is that the long-run

"steady-state" effect of money on real output becomes smaller, with the lower SE

band near zero.

c. Choice of Instrumental Variables and Method of System Estimation

Three specifications with different sets of instrumental variables were estimated.

Replacing the dummy variable War with the continuous variable Warmonth (which is

equal to the number of months a country was at war in a given year) in the baseline

specification makes virtually no difference to the results for the narrow or the broad

data set. However, eliminating the Casualty variable from the baseline specification

greatly increased the standard errors of the system's coefficients. Replacing

Casualty with Warmonth also substantially increased the coefficients' standard errors

relative to the baseline specification; for the narrow data set, imposing this

specification made the behavior of the system explosive.

Re-estimating the baseline specification using GMM instead of 3SLS increased the

standard errors of the coefficients, and tended to make the SE bands of the

associated impulse-response functions somewhat larger. But most of the results

remain intact, the main difference being that for the narrow data set it is not possible

to reject the null that money has no effect on real output.

d. Changing Restriction #4

Restriction #4 states that the impact of both monetary and fiscal policy on real output

24

must work through nominal channels. This restriction has a New Keynesian flavor,

but most recent work on the real impact of fiscal policy has an RBC bent. What

would be the impact of using an RBC variant of Restriction #4? Would the results

change if money still had to work through nominal channels, but fiscal policy were

restricted to work solely through real channels? Restriction #4 was replaced with

Restriction #4':

Restriction #4': If monetary policy affects real output, it does so by changing nominal output;

if fiscal policy affects real output, it does so by directly changing real output.

The baseline results were then reestimated with the altered restriction. For both data

sets, the impact of fiscal policy becomes if anything more negative, and the effect of

monetary policy holding fiscal policy constant stays positive. An impact of fiscal

policy through RBC channels is no more visible than one through New Keynesian

channels.25

e. Summary

Qualitatively and even quantitatively consistent estimates emerge from two quite

different data sets. In both cases, monetary policy has nominal and real effects, and

fiscal policy does not. Both data sets are robust to a variety of specification changes

- including taxes as well as spending, separating military and nonmilitary spending,

changing the lag structure, using GMM instead of 3SLS, and replacing the New

Keynesian Restriction #4 with an RBC variant. The results for monetary policy are

consistent with most of the recent empirical literature, but those for fiscal policy are

25 Removing Restriction #4 without replacing it with another restriction had more disconcerting results. The output from this initial estimation was problematic for both data sets: the implied impulse-response functions were typically either explosive or had very large SE bands. Some similar problems with explosive impulse-response functions arise in Blanchard and Perotti (1998).

25

not. Probably the simplest explanation is omitted variable bias: monetary policy

usually accomodates fiscal policy, so even if fiscal policy did not matter, it will appear

to in specifications that exclude a measure of money.

7. Conclusion

This paper points to several avenues for further research. First, reevaluation of the

effectiveness of fiscal policy from a structural VAR approach is already underway

(Blanchard and Perotti [1998]) and can be expected to grow quickly along all

dimensions in the near future. Second, while the current paper diverges from Romer

and Romer (1994a) and Ramey and Shapiro (1997) in precise choice of instruments,

further study of fiscal policy's impact using their narrative methodology would be

profitable. Third, case studies of wars, investigating the extent to which monetary

factors provide a full explanation for any wartime boom, may make the general

quantitative results reached here more plausible. Discretionary fiscal policy has only

been typically rather than universally abandoned, as Japan's recent $128 billion

fiscal stimulus package shows.26 Its critical examination remains a question of

practical as well as theoretical interest.

The main finding here is that expansionary fiscal policy does not appear to have

positive real or even nominal effects, whereas monetary policy has a clear nominal

impact that at least in the short-run translates into a real impact as well. From one

perspective, these findings should not be controversial: the results for monetary

policy are quite consistent with recent findings in the structural VAR literature, and

fiscal policy as a stabilization tool has largely fallen out of favor. However, there are

more novel implications: First, models in which monetary policy has real effects due 26 See e.g. "Recovery Package Unveiled," at http://www.abcnews.com/sections/business/DailyNews/japan980424/index.html.

26

to nominal rigidity cannot be readily extended to infer expansionary effects for fiscal

policy, since there is little evidence that fiscal policy even affects nominal output.

Second, the recent shift in stabilization policy toward exclusive reliance on monetary

policy, while often justified on political grounds, has a more fundamental argument in

its favor: money can affect overall output, while fiscal policy only seems to change

the composition of output.

27

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Table 1a: (Narrow Data Set) Regression of Nominal Output Growth, Real Output Growth, Money Supply Growth, and Government Spending as a Fraction of Output on War-Related Variables, Controlling for Country and Year EffectsAll variables expressed in percentage-point terms.Variable Nominal Output

GrowthReal Output Growth

Money Supply Growth

Gfrac

War 0.995(0.619)

0.088(0.390)

0.727(0.708)

4.645(0.620)

R2 0.529 0.276 0.449 0.729Domwar 4.386

(1.474)-6.912 (0.903)

6.557(1.679)

6.743(1.478)

Forwar 0.618(0.636)

0.867(0.389)

0.078(0.724)

4.412(0.637)

R2 0.532 0.318 0.456 0.729SEs below coefficient

Years:1884-1988 # Countries: 15N=1288 Missing Observations: 287

Table 1b: (Broad Data Set) Regression of Nominal Output Growth, Real Output Growth, Money Supply Growth, and Government Spending as a Fraction of Output on War-Related Variables, Controlling for Country and Year EffectsAll variables expressed in percentage-point terms.Variable Nominal

Output GrowthReal Output Growth

Money Supply Growth

Gfrac

War -1.706 (0.952)

-0.790 (0.744)

1.212(0.961)

0.865(0.470)

R2 0.434 0.221 0.419 0.768Domwar -4.429

(1.136)-2.486 (0.890)

0.008(1.152)

0.408(0.563)

Forwar 4.460(1.706)

3.050(1.337)

3.939(1.730)

1.898(0.846)

R2 0.440 0.226 0.420 0.769SEs below coefficient

Years:1953-1992 # Countries: 69N=2156 Missing Observations: 604

Table 1c: (Broad Data Set) Regression of Military and Non-Military Spending as a Fraction of Output on War-Related Variables, Controlling for Country and Year EffectsAll variables expressed in percentage-point terms.Variable Military Spending Non-Military

SpendingDomwar 1.091

(0.203)-1.524 (0.450)

Forwar 3.572(0.304)

-0.469 (0.750)

R2 0.731 0.760SEs below coefficient

Years:1884-1988 # Countries: 69N=2148 Missing Observations: 612

31

Table 2a: (Narrow Data Set)3SLS Estimation of Nominal and Real Output EquationsVariable Coef SE tstat

Nominal Output EstimationR(t) 1.200745 0.104777 11.46005M(t) 0.495818 0.097603 5.079933F(t) -0.265185 0.107138 -2.475160

N(t-1) 0.577617 0.037391 15.44796R(t-1) -0.591385 0.045324 -13.04808M(t-1) -0.121471 0.034192 -3.552594F(t-1) 0.142846 0.041247 3.463171N(t-2) -0.158639 0.037170 -4.267939R(t-2) 0.093634 0.043847 2.135490M(t-2) 0.027856 0.019741 1.411081F(t-2) 0.070369 0.041277 1.704802N(t-3) 0.112662 0.032025 3.517875R(t-3) -0.206061 0.041083 -5.015756M(t-3) -0.003114 0.017749 -0.175457F(t-3) -0.013134 0.037221 -0.352873

R-squared 0.660350 Mean dependent var 7.615924Adjusted R-squared 0.621205 S.D. dependent var 9.021451S.E. of regression 5.552373 Observations: 1288

Real Output EstimationN(t) 0.704612 0.076585 9.200372

N(t-1) -0.499516 0.050197 -9.951121R(t-1) 0.445416 0.037628 11.83739N(t-2) 0.083758 0.027969 2.994718R(t-2) -0.048813 0.033967 -1.437072N(t-3) -0.075932 0.025854 -2.937000R(t-3) 0.136175 0.033412 4.075674

Forcas(t) 0.082468 0.274911 0.299980Domcas(t) -0.104364 0.495857 -0.210473Forcas(t-1) 0.700155 0.330010 2.121617

Domcas(t-1) 0.751509 0.773273 0.971854Forcas(t-2) -1.089327 0.340610 -3.198167

Domcas(t-2) -2.940801 0.864544 -3.401562Forcas(t-3) 0.289115 0.262563 1.101126

Domcas(t-3) 1.210091 0.556000 2.176421R-squared 0.281192 Mean dependent var 3.085681Adjusted R-squared 0.198635 S.D. dependent var 4.583784S.E. of regression 4.103358 Observations: 1292

32

Table 2b: (Broad Data Set)3SLS Estimation of Nominal and Real Output EquationsVariable Coef SE tstat

Nominal Output EstimationR(t) 0.966085 0.153112 6.309650M(t) 0.443381 0.194392 2.280858F(t) -0.439386 0.369080 -1.190491

N(t-1) 0.309061 0.050175 6.159624R(t-1) -0.341070 0.047562 -7.171008M(t-1) -0.026711 0.041400 -0.645197F(t-1) -0.123066 0.107139 -1.148655N(t-2) -0.086859 0.030842 -2.816272R(t-2) 0.098329 0.037260 2.638966M(t-2) -0.004676 0.019434 -0.240600F(t-2) -0.015770 0.060024 -0.262731N(t-3) 0.104080 0.035549 2.927767R(t-3) -0.034063 0.030522 -1.116027M(t-3) -0.016766 0.018405 -0.910980F(t-3) 0.006052 0.054405 0.111241

R-squared 0.505141 Mean dependent var 14.65495Adjusted R-squared 0.475444 S.D. dependent var 14.85905S.E. of regression 10.76185 Observations: 2156

Real Output EstimationN(t) 0.615211 0.095939 6.412506

N(t-1) -0.263055 0.046789 -5.622168R(t-1) 0.309255 0.033101 9.342663N(t-2) 0.035725 0.020993 1.701785R(t-2) -0.096434 0.024448 -3.944466N(t-3) -0.107852 0.026081 -4.135289R(t-3) -0.000201 0.026137 -0.007706

Forcas(t) 1.511152 3.809107 0.396721Domcas(t) -1.281730 0.557687 -2.298296Forcas(t-1) 1.344743 4.723831 0.284672

Domcas(t-1) -0.317483 0.521675 -0.608583Forcas(t-2) -1.603343 4.738861 -0.338339

Domcas(t-2) 0.177427 0.359614 0.493382Forcas(t-3) 0.173129 3.330066 0.051990

Domcas(t-3) 0.604816 0.388558 1.556565R-squared 0.248563 Mean dependent var 4.607469Adjusted R-squared 0.203844 S.D. dependent var 9.925991S.E. of regression 8.856723 Observations: 2173

33